Water Supply Prediction Based on Initialized Attention Residual Network
This work addresses water supply forecasting for water plants, which is incremental as it adapts existing neural network techniques to a specific domain.
The paper tackles the problem of real-time water supply prediction by proposing an Initialized Attention Residual Network (IARN) that combines attention and residual modules, achieving state-of-the-art performance in accuracy, robustness, and generalization on several datasets.
Real-time and accurate water supply forecast is crucial for water plant. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, called Initialized Attention Residual Network (IARN), which is combined with an attention module and residual modules. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN)to recede the disturb from other factors, relieve the limitation of memory size and get a more credible results. Our method achieves state-of-the-art performance on several data sets, in terms of accuracy, robustness and generalization ability.